Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence, autonomous driving, and cultural heritage preservation. However, point cloud data are distributed irregularly and discontinuously in spatial and temporal domains, where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem. In this paper, we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression. The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis. Then, it introduces a prediction method where both intra-frame and inter-frame point clouds are available, by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm. Finally, the few prediction residual is efficiently compressed with optimal context-guided and adaptive fast-mode arithmetic coding techniques. Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information, and is suitable for 3D point cloud compression applicable to various types of scenes.